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diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/sofm/util/QuasiSigmoidDecayFunction.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/sofm/util/QuasiSigmoidDecayFunction.java
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+++ b/src/main/java/org/apache/commons/math3/ml/neuralnet/sofm/util/QuasiSigmoidDecayFunction.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math3.ml.neuralnet.sofm.util;
+
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
+import org.apache.commons.math3.exception.NumberIsTooLargeException;
+import org.apache.commons.math3.analysis.function.Logistic;
+
+/**
+ * Decay function whose shape is similar to a sigmoid.
+ * <br/>
+ * Class is immutable.
+ *
+ * @since 3.3
+ */
+public class QuasiSigmoidDecayFunction {
+    /** Sigmoid. */
+    private final Logistic sigmoid;
+    /** See {@link #value(long)}. */
+    private final double scale;
+
+    /**
+     * Creates an instance.
+     * The function {@code f} will have the following properties:
+     * <ul>
+     *  <li>{@code f(0) = initValue}</li>
+     *  <li>{@code numCall} is the inflexion point</li>
+     *  <li>{@code slope = f'(numCall)}</li>
+     * </ul>
+     *
+     * @param initValue Initial value, i.e. {@link #value(long) value(0)}.
+     * @param slope Value of the function derivative at {@code numCall}.
+     * @param numCall Inflexion point.
+     * @throws NotStrictlyPositiveException if {@code initValue <= 0}.
+     * @throws NumberIsTooLargeException if {@code slope >= 0}.
+     * @throws NotStrictlyPositiveException if {@code numCall <= 0}.
+     */
+    public QuasiSigmoidDecayFunction(double initValue,
+                                     double slope,
+                                     long numCall) {
+        if (initValue <= 0) {
+            throw new NotStrictlyPositiveException(initValue);
+        }
+        if (slope >= 0) {
+            throw new NumberIsTooLargeException(slope, 0, false);
+        }
+        if (numCall <= 1) {
+            throw new NotStrictlyPositiveException(numCall);
+        }
+
+        final double k = initValue;
+        final double m = numCall;
+        final double b = 4 * slope / initValue;
+        final double q = 1;
+        final double a = 0;
+        final double n = 1;
+        sigmoid = new Logistic(k, m, b, q, a, n);
+
+        final double y0 = sigmoid.value(0);
+        scale = k / y0;
+    }
+
+    /**
+     * Computes the value of the learning factor.
+     *
+     * @param numCall Current step of the training task.
+     * @return the value of the function at {@code numCall}.
+     */
+    public double value(long numCall) {
+        return scale * sigmoid.value(numCall);
+    }
+}